Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations148
Missing cells738
Missing cells (%)19.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.0 KiB
Average record size in memory851.4 B

Variable types

Text2
DateTime1
Categorical14
Numeric8
Unsupported1

Alerts

cant_suspensiones has constant value "1.0" Constant
cant_antecedentes has constant value "1.0" Constant
Cluster_6 has constant value "4" Constant
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_Apoderado and 1 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with dcant_procesos_adjudicado and 1 other fieldsHigh correlation
cant_representante is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_socios is highly overall correlated with cant_noAutenticadoHigh correlation
dcant_procesos_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
dtotal_articulos_provee is highly overall correlated with total_articulos_proveeHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicado and 1 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
provincia is highly overall correlated with total_articulos_proveeHigh correlation
total_articulos_provee is highly overall correlated with dtotal_articulos_provee and 1 other fieldsHigh correlation
cant_representante is highly imbalanced (58.2%) Imbalance
cant_MontoLimite is highly imbalanced (61.0%) Imbalance
cant_socios has 8 (5.4%) missing values Missing
cant_apercibimientos has 148 (100.0%) missing values Missing
cant_suspensiones has 147 (99.3%) missing values Missing
cant_antecedentes has 147 (99.3%) missing values Missing
cant_Apoderado has 41 (27.7%) missing values Missing
cant_representante has 60 (40.5%) missing values Missing
cant_noAutenticado has 102 (68.9%) missing values Missing
cant_sinMontoLimite has 85 (57.4%) missing values Missing
CUIT has unique values Unique
Nombre has unique values Unique
cant_apercibimientos is an unsupported type, check if it needs cleaning or further analysis Unsupported
monto_total_adjudicado has 2 (1.4%) zeros Zeros
antiguedad has 8 (5.4%) zeros Zeros

Reproduction

Analysis started2025-06-30 18:10:12.124865
Analysis finished2025-06-30 18:10:19.679709
Duration7.55 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct148
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2025-06-30T15:10:19.839530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.986486
Min length9

Characters and Unicode

Total characters1626
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)100.0%

Sample

1st row30558766987
2nd row30715168800
3rd row30664177818
4th row30620477113
5th row30629086524
ValueCountFrequency (%)
30558766987 1
 
0.7%
30715168800 1
 
0.7%
30664177818 1
 
0.7%
30620477113 1
 
0.7%
30629086524 1
 
0.7%
33708630409 1
 
0.7%
30525366657 1
 
0.7%
30505725774 1
 
0.7%
30710910916 1
 
0.7%
30696184263 1
 
0.7%
Other values (138) 138
93.2%
2025-06-30T15:10:20.278568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 282
17.3%
3 263
16.2%
7 185
11.4%
1 162
10.0%
6 155
9.5%
8 130
8.0%
4 124
7.6%
2 115
7.1%
5 112
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 282
17.3%
3 263
16.2%
7 185
11.4%
1 162
10.0%
6 155
9.5%
8 130
8.0%
4 124
7.6%
2 115
7.1%
5 112
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 282
17.3%
3 263
16.2%
7 185
11.4%
1 162
10.0%
6 155
9.5%
8 130
8.0%
4 124
7.6%
2 115
7.1%
5 112
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 282
17.3%
3 263
16.2%
7 185
11.4%
1 162
10.0%
6 155
9.5%
8 130
8.0%
4 124
7.6%
2 115
7.1%
5 112
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Nombre
Text

Unique 

Distinct148
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2025-06-30T15:10:20.518445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length39
Mean length20.364865
Min length6

Characters and Unicode

Total characters3014
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)100.0%

Sample

1st rowEDITORIAL ALBATROS
2nd rowconstruir futuros
3rd rowFUNDACION DE LA UNIVERSIDAD NACIONAL DEL SUR
4th rowMULTICABLE S.A.
5th rowAD-HOC S.R.L.
ValueCountFrequency (%)
s.a 37
 
7.9%
srl 31
 
6.6%
s.r.l 27
 
5.7%
de 19
 
4.0%
sa 17
 
3.6%
la 9
 
1.9%
y 7
 
1.5%
argentina 6
 
1.3%
s.a.s 4
 
0.9%
group 4
 
0.9%
Other values (287) 309
65.7%
2025-06-30T15:10:20.857275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
322
 
10.7%
A 232
 
7.7%
S 231
 
7.7%
R 180
 
6.0%
. 166
 
5.5%
E 137
 
4.5%
O 136
 
4.5%
L 134
 
4.4%
I 128
 
4.2%
N 97
 
3.2%
Other values (56) 1251
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
322
 
10.7%
A 232
 
7.7%
S 231
 
7.7%
R 180
 
6.0%
. 166
 
5.5%
E 137
 
4.5%
O 136
 
4.5%
L 134
 
4.4%
I 128
 
4.2%
N 97
 
3.2%
Other values (56) 1251
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
322
 
10.7%
A 232
 
7.7%
S 231
 
7.7%
R 180
 
6.0%
. 166
 
5.5%
E 137
 
4.5%
O 136
 
4.5%
L 134
 
4.4%
I 128
 
4.2%
N 97
 
3.2%
Other values (56) 1251
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
322
 
10.7%
A 232
 
7.7%
S 231
 
7.7%
R 180
 
6.0%
. 166
 
5.5%
E 137
 
4.5%
O 136
 
4.5%
L 134
 
4.4%
I 128
 
4.2%
N 97
 
3.2%
Other values (56) 1251
41.5%
Distinct120
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2016-01-11 00:00:00
Maximum2022-09-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-30T15:10:20.935397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:21.115472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Inscripto
110 
Desactualizado doc. vencidos
25 
Desactualizado mantención
 
7
Pre Inscripto
 
5
En Evaluacion
 
1

Length

Max length28
Median length9
Mean length13.128378
Min length9

Characters and Unicode

Total characters1943
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rowInscripto
2nd rowDesactualizado doc. vencidos
3rd rowDesactualizado doc. vencidos
4th rowInscripto
5th rowDesactualizado doc. vencidos

Common Values

ValueCountFrequency (%)
Inscripto 110
74.3%
Desactualizado doc. vencidos 25
 
16.9%
Desactualizado mantención 7
 
4.7%
Pre Inscripto 5
 
3.4%
En Evaluacion 1
 
0.7%

Length

2025-06-30T15:10:21.267480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:21.326957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 115
54.5%
desactualizado 32
 
15.2%
doc 25
 
11.8%
vencidos 25
 
11.8%
mantención 7
 
3.3%
pre 5
 
2.4%
en 1
 
0.5%
evaluacion 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
c 205
10.6%
o 198
10.2%
i 180
9.3%
s 172
8.9%
n 163
 
8.4%
t 154
 
7.9%
r 120
 
6.2%
p 115
 
5.9%
I 115
 
5.9%
a 105
 
5.4%
Other values (13) 416
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 205
10.6%
o 198
10.2%
i 180
9.3%
s 172
8.9%
n 163
 
8.4%
t 154
 
7.9%
r 120
 
6.2%
p 115
 
5.9%
I 115
 
5.9%
a 105
 
5.4%
Other values (13) 416
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 205
10.6%
o 198
10.2%
i 180
9.3%
s 172
8.9%
n 163
 
8.4%
t 154
 
7.9%
r 120
 
6.2%
p 115
 
5.9%
I 115
 
5.9%
a 105
 
5.4%
Other values (13) 416
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 205
10.6%
o 198
10.2%
i 180
9.3%
s 172
8.9%
n 163
 
8.4%
t 154
 
7.9%
r 120
 
6.2%
p 115
 
5.9%
I 115
 
5.9%
a 105
 
5.4%
Other values (13) 416
21.4%

TipoSocietario
Categorical

Distinct8
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
S.R.L
61 
Sociedad Anónima
58 
Otras Formas Societarias
10 
Persona Física
 
5
Sociedades De Hecho
 
4
Other values (3)
10 

Length

Max length26
Median length24
Mean length12.135135
Min length5

Characters and Unicode

Total characters1796
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSociedad Anónima
2nd rowSociedades De Hecho
3rd rowOtras Formas Societarias
4th rowSociedad Anónima
5th rowS.R.L

Common Values

ValueCountFrequency (%)
S.R.L 61
41.2%
Sociedad Anónima 58
39.2%
Otras Formas Societarias 10
 
6.8%
Persona Física 5
 
3.4%
Sociedades De Hecho 4
 
2.7%
Cooperativas 4
 
2.7%
Organismo Publico 3
 
2.0%
PJ Extranjero Sin Sucursal 3
 
2.0%

Length

2025-06-30T15:10:21.426129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:21.504249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s.r.l 61
24.3%
sociedad 58
23.1%
anónima 58
23.1%
otras 10
 
4.0%
formas 10
 
4.0%
societarias 10
 
4.0%
persona 5
 
2.0%
física 5
 
2.0%
sociedades 4
 
1.6%
de 4
 
1.6%
Other values (8) 26
10.4%

Most occurring characters

ValueCountFrequency (%)
a 187
 
10.4%
i 158
 
8.8%
S 139
 
7.7%
n 130
 
7.2%
d 124
 
6.9%
. 122
 
6.8%
o 108
 
6.0%
103
 
5.7%
e 96
 
5.3%
c 87
 
4.8%
Other values (26) 542
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 187
 
10.4%
i 158
 
8.8%
S 139
 
7.7%
n 130
 
7.2%
d 124
 
6.9%
. 122
 
6.8%
o 108
 
6.0%
103
 
5.7%
e 96
 
5.3%
c 87
 
4.8%
Other values (26) 542
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 187
 
10.4%
i 158
 
8.8%
S 139
 
7.7%
n 130
 
7.2%
d 124
 
6.9%
. 122
 
6.8%
o 108
 
6.0%
103
 
5.7%
e 96
 
5.3%
c 87
 
4.8%
Other values (26) 542
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 187
 
10.4%
i 158
 
8.8%
S 139
 
7.7%
n 130
 
7.2%
d 124
 
6.9%
. 122
 
6.8%
o 108
 
6.0%
103
 
5.7%
e 96
 
5.3%
c 87
 
4.8%
Other values (26) 542
30.2%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201728.48
Minimum201607
Maximum202209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:21.628135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201610.35
Q1201612
median201703
Q3201708
95-th percentile202074.05
Maximum202209
Range602
Interquartile range (IQR)96

Descriptive statistics

Standard deviation135.65339
Coefficient of variation (CV)0.00067245534
Kurtosis4.4471404
Mean201728.48
Median Absolute Deviation (MAD)6.5
Skewness2.1099114
Sum29855815
Variance18401.843
MonotonicityNot monotonic
2025-06-30T15:10:21.761760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
201611 23
15.5%
201704 17
11.5%
201701 16
 
10.8%
201702 13
 
8.8%
201703 9
 
6.1%
201612 8
 
5.4%
201705 7
 
4.7%
201610 5
 
3.4%
201707 4
 
2.7%
201706 4
 
2.7%
Other values (32) 42
28.4%
ValueCountFrequency (%)
201607 2
 
1.4%
201609 1
 
0.7%
201610 5
 
3.4%
201611 23
15.5%
201612 8
 
5.4%
201701 16
10.8%
201702 13
8.8%
201703 9
 
6.1%
201704 17
11.5%
201705 7
 
4.7%
ValueCountFrequency (%)
202209 1
0.7%
202206 1
0.7%
202205 1
0.7%
202204 1
0.7%
202203 1
0.7%
202110 1
0.7%
202109 1
0.7%
202108 1
0.7%
202011 1
0.7%
202008 1
0.7%

anio_preinscripcion
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.223
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:21.878592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12016
median2017
Q32017
95-th percentile2020.65
Maximum2022
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3643715
Coefficient of variation (CV)0.00067636125
Kurtosis4.2877704
Mean2017.223
Median Absolute Deviation (MAD)0
Skewness2.0480778
Sum298549
Variance1.8615095
MonotonicityNot monotonic
2025-06-30T15:10:21.975896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 79
53.4%
2016 39
26.4%
2018 13
 
8.8%
2019 5
 
3.4%
2022 5
 
3.4%
2020 4
 
2.7%
2021 3
 
2.0%
ValueCountFrequency (%)
2016 39
26.4%
2017 79
53.4%
2018 13
 
8.8%
2019 5
 
3.4%
2020 4
 
2.7%
2021 3
 
2.0%
2022 5
 
3.4%
ValueCountFrequency (%)
2022 5
 
3.4%
2021 3
 
2.0%
2020 4
 
2.7%
2019 5
 
3.4%
2018 13
 
8.8%
2017 79
53.4%
2016 39
26.4%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9256757
Minimum1
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:22.143808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile34.5
Maximum209
Range208
Interquartile range (IQR)7

Descriptive statistics

Standard deviation21.252
Coefficient of variation (CV)2.3809962
Kurtosis55.420026
Mean8.9256757
Median Absolute Deviation (MAD)1
Skewness6.5957409
Sum1321
Variance451.6475
MonotonicityNot monotonic
2025-06-30T15:10:22.261407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 55
37.2%
2 22
 
14.9%
3 11
 
7.4%
6 8
 
5.4%
4 7
 
4.7%
5 5
 
3.4%
12 4
 
2.7%
9 3
 
2.0%
17 3
 
2.0%
8 3
 
2.0%
Other values (19) 27
18.2%
ValueCountFrequency (%)
1 55
37.2%
2 22
 
14.9%
3 11
 
7.4%
4 7
 
4.7%
5 5
 
3.4%
6 8
 
5.4%
7 1
 
0.7%
8 3
 
2.0%
9 3
 
2.0%
10 2
 
1.4%
ValueCountFrequency (%)
209 1
0.7%
91 1
0.7%
78 1
0.7%
57 1
0.7%
51 1
0.7%
45 1
0.7%
43 1
0.7%
38 1
0.7%
28 1
0.7%
26 1
0.7%

monto_total_adjudicado
Real number (ℝ)

High correlation  Zeros 

Distinct147
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58533588
Minimum0
Maximum1.9100989 × 109
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:22.360749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44560.921
Q1501709.89
median4552040
Q324238704
95-th percentile2.523927 × 108
Maximum1.9100989 × 109
Range1.9100989 × 109
Interquartile range (IQR)23736994

Descriptive statistics

Standard deviation2.126799 × 108
Coefficient of variation (CV)3.6334677
Kurtosis50.783806
Mean58533588
Median Absolute Deviation (MAD)4422417.6
Skewness6.7416776
Sum8.6629711 × 109
Variance4.5232742 × 1016
MonotonicityNot monotonic
2025-06-30T15:10:22.495288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.4%
139515.1613 1
 
0.7%
47514199 1
 
0.7%
897332.6613 1
 
0.7%
13718355.26 1
 
0.7%
129531720.6 1
 
0.7%
43707 1
 
0.7%
4155352.146 1
 
0.7%
153227947.9 1
 
0.7%
1135621.435 1
 
0.7%
Other values (137) 137
92.6%
ValueCountFrequency (%)
0 2
1.4%
0.01 1
0.7%
1912.5 1
0.7%
16790.76923 1
0.7%
29527.62 1
0.7%
36000 1
0.7%
43707 1
0.7%
46146.77419 1
0.7%
53580 1
0.7%
67961.6129 1
0.7%
ValueCountFrequency (%)
1910098932 1
0.7%
1418191729 1
0.7%
825396407.4 1
0.7%
367857550.6 1
0.7%
341587058.6 1
0.7%
335474338.9 1
0.7%
321925562.7 1
0.7%
255189952.4 1
0.7%
247197792.1 1
0.7%
176746834.4 1
0.7%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8108108
Minimum0
Maximum5
Zeros8
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:22.594761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q14
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2527594
Coefficient of variation (CV)0.32873829
Kurtosis2.9056252
Mean3.8108108
Median Absolute Deviation (MAD)0
Skewness-1.7609328
Sum564
Variance1.5694061
MonotonicityNot monotonic
2025-06-30T15:10:22.661798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 79
53.4%
5 39
26.4%
3 13
 
8.8%
0 8
 
5.4%
2 5
 
3.4%
1 4
 
2.7%
ValueCountFrequency (%)
0 8
 
5.4%
1 4
 
2.7%
2 5
 
3.4%
3 13
 
8.8%
4 79
53.4%
5 39
26.4%
ValueCountFrequency (%)
5 39
26.4%
4 79
53.4%
3 13
 
8.8%
2 5
 
3.4%
1 4
 
2.7%
0 8
 
5.4%

provincia
Categorical

High correlation 

Distinct19
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
CABA
83 
Buenos Aires
23 
Córdoba
13 
Santa Fe
 
5
San Luis
 
4
Other values (14)
20 

Length

Max length16
Median length4
Mean length6.3851351
Min length4

Characters and Unicode

Total characters945
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)6.8%

Sample

1st rowCABA
2nd rowBuenos Aires
3rd rowBuenos Aires
4th rowCABA
5th rowCABA

Common Values

ValueCountFrequency (%)
CABA 83
56.1%
Buenos Aires 23
 
15.5%
Córdoba 13
 
8.8%
Santa Fe 5
 
3.4%
San Luis 4
 
2.7%
Rio Negro 3
 
2.0%
Extranjera 3
 
2.0%
La Pampa 2
 
1.4%
Tucumán 2
 
1.4%
Neuquén 1
 
0.7%
Other values (9) 9
 
6.1%

Length

2025-06-30T15:10:22.744590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caba 83
43.7%
buenos 23
 
12.1%
aires 23
 
12.1%
córdoba 13
 
6.8%
santa 6
 
3.2%
fe 5
 
2.6%
san 5
 
2.6%
luis 4
 
2.1%
rio 3
 
1.6%
negro 3
 
1.6%
Other values (17) 22
 
11.6%

Most occurring characters

ValueCountFrequency (%)
A 189
20.0%
B 106
11.2%
C 100
10.6%
e 66
 
7.0%
s 54
 
5.7%
r 51
 
5.4%
o 48
 
5.1%
a 46
 
4.9%
n 45
 
4.8%
42
 
4.4%
Other values (28) 198
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 189
20.0%
B 106
11.2%
C 100
10.6%
e 66
 
7.0%
s 54
 
5.7%
r 51
 
5.4%
o 48
 
5.1%
a 46
 
4.9%
n 45
 
4.8%
42
 
4.4%
Other values (28) 198
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 189
20.0%
B 106
11.2%
C 100
10.6%
e 66
 
7.0%
s 54
 
5.7%
r 51
 
5.4%
o 48
 
5.1%
a 46
 
4.9%
n 45
 
4.8%
42
 
4.4%
Other values (28) 198
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 189
20.0%
B 106
11.2%
C 100
10.6%
e 66
 
7.0%
s 54
 
5.7%
r 51
 
5.4%
o 48
 
5.1%
a 46
 
4.9%
n 45
 
4.8%
42
 
4.4%
Other values (28) 198
21.0%

cant_socios
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)6.4%
Missing8
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean2.2714286
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:22.822274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum18
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9813623
Coefficient of variation (CV)0.87229786
Kurtosis31.748461
Mean2.2714286
Median Absolute Deviation (MAD)1
Skewness4.8293037
Sum318
Variance3.9257965
MonotonicityNot monotonic
2025-06-30T15:10:22.894473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 55
37.2%
1 47
31.8%
3 26
17.6%
4 4
 
2.7%
6 3
 
2.0%
5 2
 
1.4%
10 1
 
0.7%
18 1
 
0.7%
11 1
 
0.7%
(Missing) 8
 
5.4%
ValueCountFrequency (%)
1 47
31.8%
2 55
37.2%
3 26
17.6%
4 4
 
2.7%
5 2
 
1.4%
6 3
 
2.0%
10 1
 
0.7%
11 1
 
0.7%
18 1
 
0.7%
ValueCountFrequency (%)
18 1
 
0.7%
11 1
 
0.7%
10 1
 
0.7%
6 3
 
2.0%
5 2
 
1.4%
4 4
 
2.7%
3 26
17.6%
2 55
37.2%
1 47
31.8%

cant_apercibimientos
Unsupported

Missing  Rejected  Unsupported 

Missing148
Missing (%)100.0%
Memory size2.3 KiB

cant_suspensiones
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing147
Missing (%)99.3%
Memory size9.3 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
0.7%
(Missing) 147
99.3%

Length

2025-06-30T15:10:22.972591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:23.024772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

cant_antecedentes
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing147
Missing (%)99.3%
Memory size9.3 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
0.7%
(Missing) 147
99.3%

Length

2025-06-30T15:10:23.077248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:23.145357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

cant_Apoderado
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)5.6%
Missing41
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean1.635514
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:23.187331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3.7
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97509093
Coefficient of variation (CV)0.59619846
Kurtosis4.2194209
Mean1.635514
Median Absolute Deviation (MAD)0
Skewness1.9119639
Sum175
Variance0.95080233
MonotonicityNot monotonic
2025-06-30T15:10:23.261518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 64
43.2%
2 27
18.2%
3 10
 
6.8%
4 4
 
2.7%
6 1
 
0.7%
5 1
 
0.7%
(Missing) 41
27.7%
ValueCountFrequency (%)
1 64
43.2%
2 27
18.2%
3 10
 
6.8%
4 4
 
2.7%
5 1
 
0.7%
6 1
 
0.7%
ValueCountFrequency (%)
6 1
 
0.7%
5 1
 
0.7%
4 4
 
2.7%
3 10
 
6.8%
2 27
18.2%
1 64
43.2%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)3.4%
Missing60
Missing (%)40.5%
Memory size9.6 KiB
1.0
75 
2.0
12 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters264
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 75
50.7%
2.0 12
 
8.1%
3.0 1
 
0.7%
(Missing) 60
40.5%

Length

2025-06-30T15:10:23.336380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:23.418192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 75
85.2%
2.0 12
 
13.6%
3.0 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 88
33.3%
0 88
33.3%
1 75
28.4%
2 12
 
4.5%
3 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 88
33.3%
0 88
33.3%
1 75
28.4%
2 12
 
4.5%
3 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 88
33.3%
0 88
33.3%
1 75
28.4%
2 12
 
4.5%
3 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 88
33.3%
0 88
33.3%
1 75
28.4%
2 12
 
4.5%
3 1
 
0.4%

cant_autenticado
Categorical

High correlation 

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
1.0
99 
2.0
38 
3.0
10 
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters444
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1.0
2nd row1.0
3rd row6.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 99
66.9%
2.0 38
 
25.7%
3.0 10
 
6.8%
6.0 1
 
0.7%

Length

2025-06-30T15:10:23.513911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:23.584699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 99
66.9%
2.0 38
 
25.7%
3.0 10
 
6.8%
6.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 99
22.3%
2 38
 
8.6%
3 10
 
2.3%
6 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 99
22.3%
2 38
 
8.6%
3 10
 
2.3%
6 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 99
22.3%
2 38
 
8.6%
3 10
 
2.3%
6 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 99
22.3%
2 38
 
8.6%
3 10
 
2.3%
6 1
 
0.2%

cant_noAutenticado
Categorical

High correlation  Missing 

Distinct4
Distinct (%)8.7%
Missing102
Missing (%)68.9%
Memory size9.4 KiB
1.0
32 
2.0
3.0
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters138
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 32
 
21.6%
2.0 9
 
6.1%
3.0 4
 
2.7%
4.0 1
 
0.7%
(Missing) 102
68.9%

Length

2025-06-30T15:10:23.662895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:23.725393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 32
69.6%
2.0 9
 
19.6%
3.0 4
 
8.7%
4.0 1
 
2.2%

Most occurring characters

ValueCountFrequency (%)
. 46
33.3%
0 46
33.3%
1 32
23.2%
2 9
 
6.5%
3 4
 
2.9%
4 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 46
33.3%
0 46
33.3%
1 32
23.2%
2 9
 
6.5%
3 4
 
2.9%
4 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 46
33.3%
0 46
33.3%
1 32
23.2%
2 9
 
6.5%
3 4
 
2.9%
4 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 46
33.3%
0 46
33.3%
1 32
23.2%
2 9
 
6.5%
3 4
 
2.9%
4 1
 
0.7%

cant_sinMontoLimite
Categorical

Missing 

Distinct4
Distinct (%)6.3%
Missing85
Missing (%)57.4%
Memory size9.5 KiB
1.0
44 
2.0
13 
3.0
 
4
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters189
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 44
29.7%
2.0 13
 
8.8%
3.0 4
 
2.7%
4.0 2
 
1.4%
(Missing) 85
57.4%

Length

2025-06-30T15:10:23.803425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:23.886188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 44
69.8%
2.0 13
 
20.6%
3.0 4
 
6.3%
4.0 2
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 44
23.3%
2 13
 
6.9%
3 4
 
2.1%
4 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 44
23.3%
2 13
 
6.9%
3 4
 
2.1%
4 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 44
23.3%
2 13
 
6.9%
3 4
 
2.1%
4 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 44
23.3%
2 13
 
6.9%
3 4
 
2.1%
4 2
 
1.1%

cant_MontoLimite
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
1.0
118 
2.0
25 
3.0
 
3
6.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters444
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st row2.0
2nd row1.0
3rd row6.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 118
79.7%
2.0 25
 
16.9%
3.0 3
 
2.0%
6.0 1
 
0.7%
4.0 1
 
0.7%

Length

2025-06-30T15:10:23.998731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:24.086382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 118
79.7%
2.0 25
 
16.9%
3.0 3
 
2.0%
6.0 1
 
0.7%
4.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 118
26.6%
2 25
 
5.6%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 118
26.6%
2 25
 
5.6%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 118
26.6%
2 25
 
5.6%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 148
33.3%
0 148
33.3%
1 118
26.6%
2 25
 
5.6%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.304054
Minimum1
Maximum703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-06-30T15:10:24.188959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q343.25
95-th percentile204.75
Maximum703
Range702
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation101.01341
Coefficient of variation (CV)2.0080571
Kurtosis19.86692
Mean50.304054
Median Absolute Deviation (MAD)11
Skewness4.0568562
Sum7445
Variance10203.71
MonotonicityNot monotonic
2025-06-30T15:10:24.309885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
12.8%
3 11
 
7.4%
7 6
 
4.1%
8 6
 
4.1%
12 6
 
4.1%
2 5
 
3.4%
6 5
 
3.4%
9 5
 
3.4%
4 4
 
2.7%
13 4
 
2.7%
Other values (57) 77
52.0%
ValueCountFrequency (%)
1 19
12.8%
2 5
 
3.4%
3 11
7.4%
4 4
 
2.7%
5 4
 
2.7%
6 5
 
3.4%
7 6
 
4.1%
8 6
 
4.1%
9 5
 
3.4%
10 2
 
1.4%
ValueCountFrequency (%)
703 1
0.7%
619 1
0.7%
480 1
0.7%
331 1
0.7%
327 1
0.7%
243 1
0.7%
219 1
0.7%
210 1
0.7%
195 1
0.7%
163 1
0.7%
Distinct20
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
(377.939- 599.760]
13 
(13.557.176- 19.975.532]
11 
(6.702.697- 9.424.898]
11 
(46.718.747- 89.439.449]
 
9
(222.964.579- 46.172.150.151]
 
9
Other values (15)
95 

Length

Max length29
Median length24
Mean length21.486486
Min length12

Characters and Unicode

Total characters3180
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(46.718.747- 89.439.449]
2nd row(104.767- 224.078]
3rd row(890.758- 1.302.657]
4th row(13.557.176- 19.975.532]
5th row(33.011- 104.767]

Common Values

ValueCountFrequency (%)
(377.939- 599.760] 13
 
8.8%
(13.557.176- 19.975.532] 11
 
7.4%
(6.702.697- 9.424.898] 11
 
7.4%
(46.718.747- 89.439.449] 9
 
6.1%
(222.964.579- 46.172.150.151] 9
 
6.1%
(3.396.600- 4.727.330] 9
 
6.1%
(89.439.449- 222.964.579] 9
 
6.1%
(1.793.326- 2.483.085] 8
 
5.4%
(224.078- 377.939] 8
 
5.4%
(33.011- 104.767] 8
 
5.4%
Other values (10) 53
35.8%

Length

2025-06-30T15:10:24.424352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
377.939 21
 
7.1%
13.557.176 19
 
6.4%
9.424.898 19
 
6.4%
89.439.449 18
 
6.1%
599.760 18
 
6.1%
222.964.579 18
 
6.1%
19.975.532 16
 
5.4%
6.702.697 16
 
5.4%
46.718.747 15
 
5.1%
3.396.600 15
 
5.1%
Other values (11) 121
40.9%

Most occurring characters

ValueCountFrequency (%)
. 499
15.7%
7 313
9.8%
9 299
9.4%
3 232
 
7.3%
4 216
 
6.8%
2 186
 
5.8%
1 185
 
5.8%
6 182
 
5.7%
0 175
 
5.5%
5 164
 
5.2%
Other values (5) 729
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 499
15.7%
7 313
9.8%
9 299
9.4%
3 232
 
7.3%
4 216
 
6.8%
2 186
 
5.8%
1 185
 
5.8%
6 182
 
5.7%
0 175
 
5.5%
5 164
 
5.2%
Other values (5) 729
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 499
15.7%
7 313
9.8%
9 299
9.4%
3 232
 
7.3%
4 216
 
6.8%
2 186
 
5.8%
1 185
 
5.8%
6 182
 
5.7%
0 175
 
5.5%
5 164
 
5.2%
Other values (5) 729
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 499
15.7%
7 313
9.8%
9 299
9.4%
3 232
 
7.3%
4 216
 
6.8%
2 186
 
5.8%
1 185
 
5.8%
6 182
 
5.7%
0 175
 
5.5%
5 164
 
5.2%
Other values (5) 729
22.9%

dcant_procesos_adjudicado
Categorical

High correlation 

Distinct10
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
(0.999, 2.0]
77 
(2.0, 3.0]
11 
(8.0, 12.0]
11 
(19.0, 39.0]
10 
(5.0, 6.0]
Other values (5)
31 

Length

Max length14
Median length12
Mean length11.547297
Min length10

Characters and Unicode

Total characters1709
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(2.0, 3.0]
2nd row(0.999, 2.0]
3rd row(0.999, 2.0]
4th row(19.0, 39.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 77
52.0%
(2.0, 3.0] 11
 
7.4%
(8.0, 12.0] 11
 
7.4%
(19.0, 39.0] 10
 
6.8%
(5.0, 6.0] 8
 
5.4%
(12.0, 19.0] 8
 
5.4%
(39.0, 1214.0] 7
 
4.7%
(3.0, 4.0] 7
 
4.7%
(4.0, 5.0] 5
 
3.4%
(6.0, 8.0] 4
 
2.7%

Length

2025-06-30T15:10:24.519505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:24.611445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 88
29.7%
0.999 77
26.0%
12.0 19
 
6.4%
3.0 18
 
6.1%
19.0 18
 
6.1%
39.0 17
 
5.7%
8.0 15
 
5.1%
5.0 13
 
4.4%
6.0 12
 
4.1%
4.0 12
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 296
17.3%
. 296
17.3%
9 266
15.6%
( 148
8.7%
, 148
8.7%
148
8.7%
] 148
8.7%
2 114
 
6.7%
1 51
 
3.0%
3 35
 
2.0%
Other values (4) 59
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 296
17.3%
. 296
17.3%
9 266
15.6%
( 148
8.7%
, 148
8.7%
148
8.7%
] 148
8.7%
2 114
 
6.7%
1 51
 
3.0%
3 35
 
2.0%
Other values (4) 59
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 296
17.3%
. 296
17.3%
9 266
15.6%
( 148
8.7%
, 148
8.7%
148
8.7%
] 148
8.7%
2 114
 
6.7%
1 51
 
3.0%
3 35
 
2.0%
Other values (4) 59
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 296
17.3%
. 296
17.3%
9 266
15.6%
( 148
8.7%
, 148
8.7%
148
8.7%
] 148
8.7%
2 114
 
6.7%
1 51
 
3.0%
3 35
 
2.0%
Other values (4) 59
 
3.5%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
(0.999, 2.0]
24 
(11.0, 15.0]
15 
(97.6, 161.0]
14 
(29.0, 40.0]
12 
(6.0, 8.0]
12 
Other values (10)
71 

Length

Max length15
Median length12
Mean length11.695946
Min length10

Characters and Unicode

Total characters1731
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(0.999, 2.0]
2nd row(0.999, 2.0]
3rd row(4.0, 6.0]
4th row(58.0, 97.6]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 24
16.2%
(11.0, 15.0] 15
10.1%
(97.6, 161.0] 14
9.5%
(29.0, 40.0] 12
8.1%
(6.0, 8.0] 12
8.1%
(2.0, 3.0] 11
7.4%
(8.0, 11.0] 10
 
6.8%
(4.0, 6.0] 9
 
6.1%
(58.0, 97.6] 8
 
5.4%
(21.0, 29.0] 7
 
4.7%
Other values (5) 26
17.6%

Length

2025-06-30T15:10:24.713190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 35
11.8%
11.0 25
 
8.4%
0.999 24
 
8.1%
97.6 22
 
7.4%
8.0 22
 
7.4%
15.0 21
 
7.1%
6.0 21
 
7.1%
161.0 21
 
7.1%
29.0 19
 
6.4%
40.0 18
 
6.1%
Other values (6) 68
23.0%

Most occurring characters

ValueCountFrequency (%)
. 296
17.1%
0 292
16.9%
( 148
8.5%
, 148
8.5%
148
8.5%
] 148
8.5%
1 126
7.3%
9 119
6.9%
2 67
 
3.9%
6 67
 
3.9%
Other values (5) 172
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 296
17.1%
0 292
16.9%
( 148
8.5%
, 148
8.5%
148
8.5%
] 148
8.5%
1 126
7.3%
9 119
6.9%
2 67
 
3.9%
6 67
 
3.9%
Other values (5) 172
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 296
17.1%
0 292
16.9%
( 148
8.5%
, 148
8.5%
148
8.5%
] 148
8.5%
1 126
7.3%
9 119
6.9%
2 67
 
3.9%
6 67
 
3.9%
Other values (5) 172
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 296
17.1%
0 292
16.9%
( 148
8.5%
, 148
8.5%
148
8.5%
] 148
8.5%
1 126
7.3%
9 119
6.9%
2 67
 
3.9%
6 67
 
3.9%
Other values (5) 172
9.9%

Cluster_6
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
4
148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters148
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 148
100.0%

Length

2025-06-30T15:10:24.798645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:24.843811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 148
100.0%

Most occurring characters

ValueCountFrequency (%)
4 148
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 148
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 148
100.0%

Interactions

2025-06-30T15:10:18.094929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.119423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.776837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.376912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.478275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.178183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.795163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.428057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.186699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.212013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.861042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.443399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.561329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.243545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.876733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.511544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.260325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.295542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.926808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.993834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.660882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.329654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.953743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.594762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.343571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.377385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.993357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.077887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.742670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.415311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.026908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.694541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.427575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.459146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.076574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.161576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.827472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.508823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.111518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.778163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.510306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.535535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.152913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.245219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.919194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.577850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.193570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.862518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.611477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.610155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.228706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.319453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.993375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.651975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.278753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.928019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.727401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:13.699300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:14.295301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:15.395112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.078840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:16.718723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:17.343607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:18.012682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-30T15:10:25.175444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_sociosdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.2620.1420.1260.0000.0000.0720.0000.0000.0820.0000.0000.0000.0600.0420.0000.1420.0000.000
TipoSocietario0.2621.0000.1980.2150.0790.0000.2310.0000.1570.0000.2230.0000.0840.0530.0000.0000.1980.3850.000
anio_preinscripcion0.1420.1981.000-1.000-0.0840.0000.0000.000-0.2980.0000.000-0.0990.0000.0000.074-0.2000.9140.262-0.093
antiguedad0.1260.215-1.0001.0000.0840.0000.0000.0000.2980.0000.0000.1010.0000.0000.0500.199-0.9140.2030.093
cant_Apoderado0.0000.079-0.0840.0841.0000.7280.6410.6780.1840.0000.4830.2650.0000.2690.2230.297-0.0410.0000.161
cant_MontoLimite0.0000.0000.0000.0000.7281.0000.6020.5640.0000.1920.0000.4980.0000.1930.1400.0680.0000.0000.000
cant_autenticado0.0720.2310.0000.0000.6410.6021.0000.0530.0580.0000.1700.0750.0410.1500.1700.1300.0000.0000.000
cant_noAutenticado0.0000.0000.0000.0000.6780.5640.0531.0000.0000.3320.4830.5470.0000.0000.0000.3650.0000.0000.000
cant_procesos_adjudicado0.0000.157-0.2980.2980.1840.0000.0580.0001.0000.2600.1550.0340.5540.0000.0940.622-0.3390.0000.326
cant_representante0.0820.0000.0000.0000.0000.1920.0000.3320.2601.0000.0000.0000.2070.0000.0000.6950.0000.0000.116
cant_sinMontoLimite0.0000.2230.0000.0000.4830.0000.1700.4830.1550.0001.0000.0000.0920.0000.3630.3570.0000.0000.000
cant_socios0.0000.000-0.0990.1010.2650.4980.0750.5470.0340.0000.0001.0000.0970.0370.1330.143-0.1030.000-0.002
dcant_procesos_adjudicado0.0000.0840.0000.0000.0000.0000.0410.0000.5540.2070.0920.0971.0000.1920.0000.2610.0000.0000.160
dmonto_total_adjudicado0.0600.0530.0000.0000.2690.1930.1500.0000.0000.0000.0000.0370.1921.0000.1340.3520.0000.1890.000
dtotal_articulos_provee0.0420.0000.0740.0500.2230.1400.1700.0000.0940.0000.3630.1330.0000.1341.0000.0000.0740.1820.529
monto_total_adjudicado0.0000.000-0.2000.1990.2970.0680.1300.3650.6220.6950.3570.1430.2610.3520.0001.000-0.2480.0000.203
periodo_preinscripcion0.1420.1980.914-0.914-0.0410.0000.0000.000-0.3390.0000.000-0.1030.0000.0000.074-0.2481.0000.262-0.058
provincia0.0000.3850.2620.2030.0000.0000.0000.0000.0000.0000.0000.0000.0000.1890.1820.0000.2621.0000.618
total_articulos_provee0.0000.000-0.0930.0930.1610.0000.0000.0000.3260.1160.000-0.0020.1600.0000.5290.203-0.0580.6181.000

Missing values

2025-06-30T15:10:19.111333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T15:10:19.327538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T15:10:19.570295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
20130558766987EDITORIAL ALBATROS15/11/2016InscriptoSociedad Anónima20161120163.04.751420e+075.0CABA3.0NaNNaNNaN1.01.01.01.0NaN2.01.0(46.718.747- 89.439.449](2.0, 3.0](0.999, 2.0]4
20430715168800construir futuros26/07/2016Desactualizado doc. vencidosSociedades De Hecho20160720162.01.395152e+055.0Buenos Aires1.0NaNNaNNaN1.0NaN1.0NaNNaN1.01.0(104.767- 224.078](0.999, 2.0](0.999, 2.0]4
33530664177818FUNDACION DE LA UNIVERSIDAD NACIONAL DEL SUR13/06/2017Desactualizado doc. vencidosOtras Formas Societarias20170620172.08.973327e+054.0Buenos Aires1.0NaNNaNNaN6.01.06.01.01.06.06.0(890.758- 1.302.657](0.999, 2.0](4.0, 6.0]4
38230620477113MULTICABLE S.A.12/01/2017InscriptoSociedad Anónima201701201728.01.371836e+074.0CABA1.0NaNNaNNaN1.0NaN1.0NaNNaN1.070.0(13.557.176- 19.975.532](19.0, 39.0](58.0, 97.6]4
38630629086524AD-HOC S.R.L.21/06/2017Desactualizado doc. vencidosS.R.L20170620171.04.370700e+044.0CABA1.0NaNNaNNaN1.01.01.01.01.01.01.0(33.011- 104.767](0.999, 2.0](0.999, 2.0]4
41533708630409ITSG SRL14/11/2016InscriptoS.R.L201611201622.01.295317e+085.0CABA1.0NaNNaNNaN1.0NaN1.0NaNNaN1.0151.0(89.439.449- 222.964.579](19.0, 39.0](97.6, 161.0]4
42330525366657Refinitiv Ltd03/01/2017InscriptoOtras Formas Societarias201701201717.09.995957e+064.0CABA1.0NaNNaNNaN2.0NaN2.0NaN1.01.03.0(9.424.898- 13.557.176](12.0, 19.0](2.0, 3.0]4
45830505725774FUNDARG S.R.L07/07/2017InscriptoS.R.L20170720174.04.155352e+064.0Córdoba2.0NaNNaNNaN1.01.02.0NaNNaN2.07.0(3.396.600- 4.727.330](3.0, 4.0](6.0, 8.0]4
60230710910916Cooperativa de Trabajo Dario Santillan Limitada08/11/2016InscriptoCooperativas201611201617.01.532279e+085.0CABA3.0NaNNaNNaN1.0NaN1.0NaNNaN1.0114.0(89.439.449- 222.964.579](12.0, 19.0](97.6, 161.0]4
74730696184263MB-GEOAIR SA03/05/2017InscriptoSociedad Anónima20170520171.01.135621e+064.0CABA2.0NaNNaNNaN1.0NaN1.0NaNNaN1.049.0(890.758- 1.302.657](0.999, 2.0](40.0, 58.0]4
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
882030715853465PRODUCCIONES LA MAQUINA S.R.L.18/04/2018InscriptoS.R.L20180420182.01.571110e+073.0CABA2.0NaNNaNNaN1.01.01.01.01.01.012.0(13.557.176- 19.975.532](0.999, 2.0](11.0, 15.0]4
885330714777439ANGEL BOHORQUEZ y DEL HIERRO ROBERTO SH11/07/2019InscriptoSociedades De Hecho20190720191.06.425723e+062.0Buenos Aires2.0NaNNaNNaN2.0NaN2.0NaNNaN2.011.0(4.727.330- 6.702.697](0.999, 2.0](8.0, 11.0]4
941530638733427ALDO BOLZAN Y HNOS SRL18/04/2017InscriptoS.R.L20170420171.06.763057e+054.0Córdoba2.0NaNNaNNaNNaN2.01.01.0NaN2.026.0(599.760- 890.758](0.999, 2.0](21.0, 29.0]4
957130711460418PATAGONIA STEEL S.R.L.07/09/2021InscriptoS.R.L20210920211.08.137786e+050.0Chubut1.0NaNNaNNaNNaN1.01.0NaNNaN1.0219.0(599.760- 890.758](0.999, 2.0](161.0, 345.0]4
959130709902527CASEROS PARK S.A.24/11/2016InscriptoSociedad Anónima20161120161.02.880000e+075.0CABA3.0NaNNaNNaN1.0NaN1.0NaNNaN1.01.0(19.975.532- 30.451.916](0.999, 2.0](0.999, 2.0]4
969330716844583GRUPO WG S.A.S20/04/2022InscriptoOtras Formas Societarias20220420222.02.508003e+050.0Corrientes2.0NaNNaNNaNNaN1.01.0NaNNaN1.0703.0(224.078- 377.939](0.999, 2.0](345.0, 6993.0]4
983130716327279Diversitas S.R.L.09/05/2022InscriptoS.R.L20220520221.04.050000e+050.0CABA2.0NaNNaNNaNNaN1.01.0NaNNaN1.024.0(377.939- 599.760](0.999, 2.0](21.0, 29.0]4
990730700951738MVS SA15/03/2022InscriptoSociedad Anónima20220320221.04.371429e+060.0CABA2.0NaNNaNNaN2.0NaN1.01.0NaN2.04.0(3.396.600- 4.727.330](0.999, 2.0](3.0, 4.0]4
997130712369120PINTURERIA ARGENTINA SH24/06/2022InscriptoSociedades De Hecho20220620221.04.614677e+040.0Rio Negro3.0NaNNaNNaNNaN1.01.0NaNNaN1.033.0(33.011- 104.767](0.999, 2.0](29.0, 40.0]4
1007330700503891GIJON SA07/09/2022InscriptoSociedad Anónima20220920221.01.119596e+070.0CABA3.0NaNNaNNaN1.0NaN1.0NaNNaN1.01.0(9.424.898- 13.557.176](0.999, 2.0](0.999, 2.0]4